An Exploration of Complexity

01/27/25 • 99 min

Modern software development feels so complex. In this episode, Ian Wester-Lopshire is joined by Kris Brandow, Matthew Sanabria, and Angelica Hill to discuss complexity. They propose hypotheses for the root cause of complexity, explore the ways complexity affects our technology and organizations, and ponder if artificial intelligence can aid us in reducing complexity. As always, we've got some great unpopular opinions at the end.

As always thanks for tuning in and happy listening!

This episode is a pilot for a planned mini-series on complexity. We'd like to explore complexity deeper by having conversations with and interviewing a diverse set of people. Do you have thoughts about complexity, know someone who thinks deeply about this topic, or have something you'd like to add? Reach out to us on social media and let us know!

Notes & Links:

Chapters:

(00:00) - Intro
(00:31) - How are we doing?
(01:24) - Where did complexity come from?
(02:53) - Ian's Hypothesis
(07:00) - Kris' (Hot Take) Hypothesis
(14:38) - FAANG & Engineering Excellence
(19:21) - Do better practices lead to complexity?
(24:02) - The Cloud's enablement of complexity
(24:39) - Some of the complexity is worth it
(26:01) - Systems become more complex over time
(27:33) - Moving Faster & Thinking Less
(31:18) - Accidental complexity
(31:47) - Where else does complexity surface?
(32:03) - Human Organizations & Trading Complexities
(37:46) - Documenting Decisions
(38:20) - Hypoethsis: Bad information organization is the root of complexity
(49:01) - Can AI be our librarian?
(54:39) - Search is not a text box
(55:57) - Machine learning for indexing
(01:00:17) - Can AI write our code?
(01:05:44) - Easy & Hard Learning
(01:12:27) - Hypothesis: mixed and unclear terminology
(01:14:46) - Concise Hyopthesis of Complexity
(01:18:05) - Letters by mail?!
(01:18:56) - Unpopular Opinions
(01:19:14) - Matthew's Unpop
(01:23:10) - Ian's Unpop
(01:28:30) - Kris' Unpop
(01:35:47) - Fallthrough & CPU.fm
(01:37:39) - Complexity Mini-Series
(01:38:26) - Outro

Hosts

Socials:

As always thanks for tuning in and happy listening!

Notes & Links:

Chapters:

(00:00) - Intro
(00:31) - How are we doing?
(01:24) - Where did complexity come from?
(02:53) - Ian's Hypothesis
(07:00) - Kris' (Hot Take) Hypothesis
(14:38) - FAANG & Engineering Excellence
(19:21) - Do better practices lead to complexity?
(24:02) - The Cloud's enablement of complexity
(24:39) - Some of the complexity is worth it
(26:01) - Systems become more complex over time
(27:33) - Moving Faster & Thinking Less
(31:18) - Accidental complexity
(31:47) - Where else does complexity surface?
(32:03) - Human Organizations & Trading Complexities
(37:46) - Documenting Decisions
(38:20) - Hypoethsis: Bad information organization is the root of complexity
(49:01) - Can AI be our librarian?
(54:39) - Search is not a text box
(55:57) - Machine learning for indexing
(01:00:17) - Can AI write our code?
(01:05:44) - Easy & Hard Learning
(01:12:27) - Hypothesis: mixed and unclear terminology
(01:14:46) - Concise Hyopthesis of Complexity
(01:18:05) - Letters by mail?!
(01:18:56) - Unpopular Opinions
(01:19:14) - Matthew's Unpop
(01:23:10) - Ian's Unpop
(01:28:30) - Kris' Unpop
(01:35:47) - Fallthrough & CPU.fm
(01:37:39) - Complexity Mini-Series
(01:38:26) - Outro

Hosts

Socials:

Previous Episode

Do We Think You Should Learn To Code?

For years we've been told that everyone needs to learn to code. As career software people, what are our thoughts on this? Should you learn to code? Is coding the right career for you? In this first episode of a two part series, the panel discusses how they learned to code, whether they think everyone should learn to code, and of course they offer up some unpopular opinions.

And if you're thinking, "I already know how to code", don't worry, this episode contains valuable advice for you as well.

As always thanks for tuning in and happy listening!

Notes & Links:

Ben Eater's YouTube Channel (from 53:00)
John Carmack's Fast Inverse Square Root (from 1:01:47)
- This is actually from Quake, not Doom.
Change Interviews #581 It's Not Always DNS (from 1:04:15)

Chapters:

(00:00) - Intro
(01:33) - How're we doing?
(04:41) - Why did we learn to code?
(06:10) - Why Kris learned to code
(07:44) - Why Ian learned to code
(08:54) - Why Dylan learned to code
(10:36) - Why Matthew learned to code
(14:22) - Computer Assembly Not Required
(16:51) - Tangent: Why Are USB-C Cables?
(17:36) - Should you focus on a specific language?
(22:45) - Do you want to solve problems?
(24:53) - Coding & Woodworking
(27:09) - Curiosity & Itches that need scratching
(31:07) - What do we mean when we say "code"?
(32:34) - You should learn Excel
(39:35) - Coding Is Configuring Computers
(43:32) - Discrete Math, Not Binary
(47:03) - Learn the low level stuff... eventually
(53:42) - The desire to learn
(57:41) - Sidequest: Networking
(01:05:52) - Learning Design & Being Load Bearing
(01:12:10) - Continual Learning
(01:14:33) - What of AI and Automation?
(01:21:49) - Share What You Learn
(01:24:24) - Should You Learn To Code?
(01:24:57) - Teaser: Episode 5 - Is Coding The Career For You?
(01:26:02) - Unpopular Opinions
(01:26:08) - Matthew's Unpop
(01:28:51) - Ian's Unpop
(01:29:29) - Dylan's Unpop
(01:33:52) - Kris' Unpop
(01:36:50) - Tangent: Chopper?
(01:37:27) - Outro

Hosts

Socials:

Next Episode

Navigating A Career In Tech

How much do you know about a career in coding? Perhaps you're already a well versed professional software person. Or maybe you're someone who's just starting out. Either way, you're going to need some help along your entire technology career. In this episode, which is part 2 of a 2 part series, the panel is chatting with Chris Allaire to talk about all things tech recruiting! They discuss what makes one a good candidate, why a recruiter is useful, whether recruiting can be replaced with AI, and so much more. As always, we've got some great unpopular opinions at the end.

Thanks for tuning in and happy listening!

Notes & Links: